Mechanical performance of ITO/Ag/ITO multilayer films deposited on glass substrate by RF and DC magnetron sputtering

ITO/Ag/ITO multilayer thin films have been a potential substitute of the conventional single-layer transparent conducting film. Nevertheless, the mechanical stability under preparation and in-service conditions still limits their applications and developments. In this paper, the influences of different structural properties as well as layer structure on both surface morphological properties and mechanical properties of the ITO/Ag/ITO multilayer thin films in comparison with commercial single-layer ITO thin film were systematically investigated. The results demonstrate that, i) the tri-layer composite has large impacts on the preferential orientation, and exhibits the decreased values of surface roughness, net lattice distortion and residual stress; ii) the increased hardness (H) and decreased Young's modulus (E) for full annealed ITO/Ag/ITO multilayer films indicate that it is possible to tailor mechanical properties of the materials by manufacturing multilayer composite; iii) the ITO/Ag/ITO multilayer thin film exhibits remarkable improvements in wear resistance with the increase of annealing temperature, which is mainly attributed to the increased ratios of H/E and H³/E².     

Investigation on thermal conductivity and mechanical properties of Si3N4 ceramics via one-step sintering

High thermal conductivity Si₃N₄ ceramics were fabricated using a one-step method consisting of reaction-bonded Si₃N₄ (RBSN) and post-sintering. The influence of Si content on nitridation rate, β/(α+β) phase rate, thermal conductivity and mechanical properties was investigated in this work. It is of special interest to note that the thermal conductivity showed a tendency to increase first and then decrease with increasing Si content. This experimental result shows that the optimal thermal conductivity and fracture toughness were obtained to be 66 W (m K)^-1 and 12.0 MPa m^1/2, respectively. As a comparison, the nitridation rate and β/(α+β) phase rate in a static pressure nitriding system, i.e., 97% (MS10), 97% (MS15), 97% (MS20) and 8.3% (MS10), 8.3% (MS15), 8.9% (MS20), respectively, have obvious advantages over those in a flowing nitriding system, i.e., 91% (MS10), 91% (MS15), 93% (MS20) and 3.1% (MS10), 3.3% (MS15), 3.3% (MS20), respectively. Moreover, high lattice integrity of the β-Si₃N₄ phase was observed, which can effectively confine O atoms into the β-Si₃N₄ lattice using MgO as a sintering additive. This result indicates that one-step sintering can provide a new route to prepare Si₃N₄ ceramics with a good combination of thermal conductivity and mechanical properties.     

Hydrogen sulfide gas detection by Au-decorated ZnO nanotube: a computational study and comparison to experimental observations

Based on the experimental reports, Au-decoration on the ZnO nanostructures dramatically increases the electronic sensitivity to H₂S gas. In the current study, we computationally scrutinized the mechanism of Au-decoration on a ZnO nanotube (ZON) and the influence on its sensing behavior toward H₂S gas. The intrinsic ZON weakly interacted with the H₂S gas with an adsorption energy of ?11.2 kcal/mol. The interaction showed no effect on the HOMO–LUMO gap and conductivity of ZON. The predicted response of intrinsic ZON toward H₂S gas is 6.3, which increases to 78.1 by the Au-decoration at 298 K. The corresponding experimental values are about 5.0 and 80.0, indicating excellent agreement with our findings. We showed that the Au atom catalyzes the reaction 3O₂?+?2H₂S?→?2SO₂?+?2H₂O. Our calculated energy barrier (at 298 K) is about 12.3 kcal/mol for this reaction. The gap and electrical conductance Au-ZON largely changed by this reaction are attributed to the electron donation and back-donation processes. The obtained recovery time is about 1.35 ms for desorption of generated gases from the surface of the Au-ZON sensor.     

Smart IoT data platform in hospital and postoperative analgesic effects of orthopedic patients

IOT test this large donor control engineering data can relieve postoperative pain is discussed. One human operation, closed-loop, postoperative analgesic things large data system is designed to alleviate chronic pain. In this system the need of pain relief, when the patient presses the button. Computer interface buttons, the patient's IOT large data after pump. Patients with pain models have been developed to describe the kinetics of chronic pain in people's physiological and psychological reactions. Further including analgesic pharmacokinetic model. Pain model parameters have been adjusted, so that large data things validated self-management of pain behavior mimics a real button, presses the record. Unfortunately, if the adhesion is removed, the inflammation process begins; it is the foundation of a new adhesion causes, creating a vicious cycle difficult to break. Is achieved by electrical stimulation therapy of the small intestine contraction is applied. The results have not been entirely successful, it does not complete the verification of transcutaneous electrical nerve stimulation. However, this idea, a new method is reliable become the treatment of adhesive intestinal obstruction. Effective use of long things big data, as of this site, are, first aid and coherent knowledge of post-operative care, and people to help taking the right medicine for the patient, such research index regret believe that to reduce the occurrence of cans distinguish the analgesic efficacy of the use of different large amounts and compositions of the networking data six hundred and seventy and 9 vertical abdomen, spine and limbs procedures.     

Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels

Having accurate information about the hydrogen solubility in hydrocarbon fuels and feedstocks is very important in petroleum refineries and coal processing plants. In the present work, extreme gradient boosting (XGBoost), multi-layer perceptron (MLP) trained with Levenberg–Marquardt (LM) algorithm, adaptive boosting support vector regression (AdaBoost?SVR), and a memory-efficient gradient boosting tree system on adaptive compact distributions (LiteMORT) as four novel machine learning methods were used for estimating the hydrogen solubility in hydrocarbon fuels. To achieve this goal, a database containing 445 experimental data of hydrogen solubilities in 17 various hydrocarbon fuels/feedstocks was collected in wide-spread ranges of operating pressures and temperatures. These hydrocarbon fuels include petroleum fractions, refinery products, coal liquids, bitumen, and shale oil. Input parameters of the models are temperature and pressure along with density at 20 °C, molecular weight, and weight percentage of carbon (C) and hydrogen (H) of hydrocarbon fuels. XGBoost showed the highest accuracy compared to the other models with an overall mean absolute percent relative error of 1.41% and coefficient of determination (R²) of 0.9998. Also, seven equations of state (EOSs) were used to predict hydrogen solubilities in hydrocarbon fuels. The 2- and 3-parameter Soave-Redlich-Kwong EOS rendered the best estimates for hydrogen solubilities among the EOSs. Moreover, sensitivity analysis indicated that pressure owns the highest influence on hydrogen solubilities in hydrocarbon fuels and then temperature and hydrogen weight percent of the hydrocarbon fuels are ranked, respectively. Finally, Leverage approach results exhibited that the XGBoost model could be well trusted to estimate the hydrogen solubility in hydrocarbon fuels.     

Magnetic field-enhanced photocatalytic nitrogen fixation over defect-rich ferroelectric Bi2WO6

Photocatalytic N₂ fixation is a promising and sustainable manufacturing process of ammonia (NH₃); however, the NH₃ production rate by this method is very low, thus severely restricting further application of this sustainable technology. Therefore, developing an efficient photocatalyst for N₂ fixation under mild conditions is urgently required. Herein, ferroelectric Bi₂WO₆ materials with different surface oxygen defects were prepared, and the concentration of corresponding defects was controlled by adjusting the thermal reduction time. The abundant oxygen defects in Bi₂WO₆ can provide more reactive sites to promote the effective adsorption of N₂, and the photogenerated charge carrier can be efficiently separated benefiting from the internal electric field. These would weaken the N₂ triple bond and reduce the activation energy barrier for the conversion of N₂ to NH₃ under mild conditions. In the absence of sacrificial agents and cocatalysts, the optimized Bi₂WO₆ with oxygen defects shows an indigenous NH₃ yield of 132.175 μmol·g^-1·L^-1·h^-1, which is more than two times higher than that of the original Bi₂WO₆. Surprisingly, the Bi₂WO₆ with oxygen defects produced more than eight times NH₃ (471.13 μmol·g^-1·L^-1·h^-1) than that of the original Bi₂WO₆ when assisted by an external magnetic field, thus providing a new perspective for further enhancing the N₂ fixation performance.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.